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Managing cybersecurity and protecting data assets remain top priorities for businesses. Despite this, numerous data breaches persist due to malicious human actions, resulting in significant financial setbacks. However, many cybersecurity strategies overlook invisible or indirect threats within their scope, such as digital footprints. This paper examines the relationship between personality traits and user behavior concerning cybersecurity. The study suggests that human personality can be predicted using innovative techniques based on the digital hints individuals leave on the internet. Consequently, this information can be exploited for malicious actions against entities. As proposed, an effective strategy for improving behaviors and cultivating a security-oriented culture involves continually identifying relevant sources of cyber risks and implementing continuous awareness initiatives.

Each person’s unique traits hold valuable insights into their consumer behavior, allowing scholars and industry experts to develop innovative marketing strategies, personalized solutions, and enhanced user experiences. This study presents a conceptual framework that explores the connection between fundamental personality dimensions and users’ online shopping styles. By employing the TIPI test, a reliable and validated alternative to the Five-Factor model, individual consumer profiles are established. The results reveal a significant relationship between key personality traits and specific online shopping functionalities. To accurately forecast customers’ needs, expectations, and preferences on the Internet, we propose the implementation of two Machine Learning models, namely Decision Trees and Random Forest. According to the applied evaluation metrics, both models demonstrate fine predictions of consumer behavior based on their personality.

The determination of the energy spectra of small spin systems as for instance given by magnetic molecules is a demanding numerical problem. In this work we review numerical approaches to diagonalize the Heisenberg Hamiltonian that employ symmetries; in particular we focus on the spin-rotational symmetry SU(2) in combination with point-group symmetries. With these methods one is able to block-diagonalize the Hamiltonian and thus to treat spin systems of unprecedented size. Thermodynamic observables such as the magnetization are then easily evaluated. In addition it provides a spectroscopic labeling by irreducible representations that can be related to selection rules which can be helpful when interpreting transitions induced by electron paramagnetic resonance, nuclear magnetic resonance or inelastic neutron scattering. It is our aim to provide the reader with detailed knowledge on how to set up such a diagonalization scheme.